Algorithms for Optimization provides a thorough introduction to optimization with an emphasis on practical algorithms designed specifically for engineering systems. Framed from an engineering perspective, the book demonstrates how to design systems that optimize a set of metrics while satisfying various constraints. It carefully develops computational approaches to address challenges such as navigating high-dimensional spaces, balancing multiple competing objectives, and incorporating uncertainty into metrics. The text explores a range of topics including derivative-based methods, local descent techniques using first- and second-order approaches, and stochastic methods that introduce randomness to the optimization process. It also delves into linear constrained optimization as well as the use of surrogate models and probabilistic surrogates to guide decision-making under uncertainty. The inclusion of detailed figures, concrete examples, and exercises, alongside an appendix introducing the Julia language and key mathematical concepts, makes this work a valuable resource for advanced undergraduates, graduate students, and professionals in various fields.
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